Policy makers believe money motivates more than it does

To motivate contributions to public goods, should policy makers employ financial incentives like taxes, fines, subsidies, and rewards? While these are widely considered as the classic policy approach, a substantial academic literature suggests the impact of financial incentives is not always positive; they can sometimes fail or even backfire. To test whether policy makers are overly bullish about financial incentives, we asked county heads, mayors, and municipal government representatives of medium-to-large towns in Germany to predict the effects of a financial incentive on COVID-19 vaccination, and tested the exact same incentive in a field experiment involving all 41,548 inhabitants (clustered in 10,032 addresses) of the German town of Ravensburg. Whereas policy makers overwhelmingly predict that the financial incentive will increase vaccination—by 15.3 percentage points on average—the same financial incentive yielded a precisely estimated null effect on vaccination. We discuss when financial incentives are most likely to fail, and conclude that it is critical to educate policy makers on the potential pitfalls of employing financial incentives to promote contributions to public goods.


Section 1: Field Experiment 1.1 Policy Context
The focus of this study was the entire resident population of the German city of Ravensburg.In Germany, all adult residents of the city are legally required to have a name and address record in the city's official registry.So, as part of this study, all adult residents in the city's registry received an official letter from the city government with information about seven public COVID-19 vaccination events organized by the city.No appointment was needed, and, as is the case for all of Germany, the COVID-19 vaccinations at these events were free of charge.
At the start of the vaccination campaign (November 5th, 2021), 61% of residents in Ravensburg and the surrounding county had received the first and second dose of the vaccine.This was below the average vaccination rate (62.5%) in the corresponding region, Baden-Wuerttemberg (1).The 7-days incidence rate per 100,000 residents was approximately 275, above the average (211) and among the highest in the region (2).On the first day of the vaccination campaign (11/5/2021), the German federal public health ministry officially amended its prior recommendation of booster vaccinations only for older and at-risk individuals and, instead, encouraged all German residents to get a booster dose (3).
Due to German laws regulating the privacy of official medical records, it is not possible to track every resident's vaccination status.As such, by documenting vaccination at public events, we have incomplete outcome data.While we can ascertain a participant was vaccinated at one of the participating events (administration of the vaccine was directly observed), we cannot ascertain that a non-attendee didn't get vaccinated elsewhere (e.g., in another city).However, since the letters sent to both treatment and control addresses advertised only a set of public events, it is reasonable to assume that any impacts of treatment would predominantly affect vaccination rates at events that participants are informed about.In Section 1.10, we address this in more detail.
Since we anticipated official government announcements during the course of our study, we preregistered that we would present our treatment effects by event, by day, and cumulatively.The following official government announcements were recorded since the start of the campaign on November 5th, 2021 (4): 11/05/2021 The federal health ministry as well as the state ministries in Germany recommend booster vaccination for all persons older than 18. 11/10/2021 Federal and state authorities decide to offer only the Moderna vaccine to persons aged 30 and older, allotting the Pfizer/BioNTech vaccine for younger persons.(There were very few doses of the Pfizer/BioNTech vaccine available in Germany, at this time.)11/17/2021 The regional health ministry of Baden-Wuerttemberg introduces the "2G rule," restricting access to most shops and restaurants to people who either 1) received the first and second dose or 2) recovered from COVID-19.11/18/2021 The Standing Committee on Vaccination, an independent expert committee in Germany, recommends booster vaccination for all persons older than 18, six months after the second dose.

Materials and Methods
The initial aim of the study was to assess the efficacy of a set of monetary incentives on people's vaccination uptake.In this manuscript, we focus on the direct effect of the financial incentive.The spillover analysis is the subject of another manuscript.
The study was conducted among the full adult population of the city of Ravensburg, Germany.All registered residents of the city (age 18 years and older) were included in the trial.As described in greater detail in our pre-registration plan, the only exclusions were the inmates of a local prison (n=257), the second largest cluster of the city (n=75), and one single-person household in a block size of one.The final study population thus included 41,548 residents nested in 10,032 address-clusters.
All registered residents of the City received a mailer in the first week of November 2021 informing them about seven public vaccination events that took place between November 13 and December 12, 2021.The letter was sent by city officials and signed by the Mayor of the city.The treatment group received a letter that included a monetary incentive of approximately $40 (i.e., 40 Euros).The incentive included an individual-level incentive of ~$20 (20 Euros) for getting vaccinated.In addition to that, a financial incentive of an additional ~$20 if more than 900 residents receive vaccinations during the seven public vaccination events was added to the letters.If recipients were already fully vaccinated (i.e., had all the recommended doses of the COVID-19 vaccine), they could still receive the community-based financial incentive if they gave their letter to another resident of the City and that other resident presented that letter while getting vaccinated at the vaccination event.The control group received the same letter as the treatment group but without the monetary incentive.A copy of the German and Englishtranslation of both letters can be found in Section 1.3.
Treatment assignment followed a two-stage randomization process.Randomization was conducted in R (version 4.1.1)using the function "sample" with the seed set to 2021.In the first stage, all housing addresses within the city were cluster-randomized into two groups (i.e., the intervention group and the control group).The cluster-level randomization was blocked by cluster size (four address-clusters per block), to account for unequal cluster sizes (5).In the second stage, one resident of each address-cluster in both groups was randomly selected (a "cluster representative").The cluster representatives in the intervention group received the intervention letter.All remaining residents were sent the control letter.
Two primary outcome measures were collected, information and vaccination uptake.In the present study, we focus on vaccination uptake only, but report the full results (which include information uptake) in a separate paper.
During the seven public vaccination events, on-site records of administered vaccinations were recorded and linked back to administrative records of the city.The vaccination events took place during the following dates: For the analysis, all residents living at the same address were treated as members of the same social network.As specified in our pre-analysis plan, our main estimator was a demographic covariate-adjusted OLS regression with block fixed effects and clustered standard errors (i.e., the overall effect).
For robustness, we re-ran all analyses using the pre-registered direct effect estimator and also a naive estimator that ignored household structure (i.e., the outcomes of those who received the treatment compared to all those who did not).Additionally, we used randomization inference (6) as an alternative means to calculate p-values.The results of these robustness checks were highly similar to that of our overall effect estimator; for more details, please.see Section 1. Vaccination uptake: This variable describes whether a Ravensburg resident was vaccinated at one of the advertised vaccination events.The specific procedure for vaccination at these events is described below.
City security staff directed people to wait in line outside of the vaccination site building (to reduce the risk of infection).Before the waitlist system was implemented (see Section 1.10 for more detail), the first individual in line was invited into the vaccination site building.After the waitlist system was implemented, the individual with the announced waitlist number was invited inside.In both cases, the individual first went to the front desk where city officials collected data about the individual to be vaccinated.Representatives of the city collected the individual's full name and address from the individual's ID.This was done in accordance with the researchers' protocol and under the oversight of one of the authors.After data collection, the individual was directed to a different table in the same shared space, where a medical professional would provide a medical consultation (e.g., explain which vaccine would be provided) and administer the vaccination.The individual could still opt out after the medical consultation; the data collection team was informed of every opt-out and flagged all opt-outs in the collected data.
(The four opt-outs had their vaccination outcome coded as zero in all of our analyses.)After vaccination, in accordance with official medical advice, the individual was directed to a monitoring room to watch for adverse (e.g., allergic) reactions to the vaccine.
Vaccination uptake was measured at each vaccination event.These data were later matched by the researchers to the list of letter recipients.
The city of Ravensburg decided to hold additional vaccination events not advertised in the letters.We analyzed this additional vaccination data in a separate paper.To check the robustness of the results reported in this paper, we re-ran all analysis with the additional outcome data and the substantive findings remained unchanged.For more details, please see (7).

Descriptive Statistics
Table S1 gives an overview of the characteristics of the participants.

Treatment Effect Heterogeneity
We investigate treatment effect heterogeneity in two ways: 1) through a series of regressions that interact the treatment indicator against each available covariate, and 2) causal forests.

Interaction Effects
As seen via the interaction terms in Table S3, treatment effects did not significantly differ across age, gender, and nationality.The same lack of heterogeneity was found in terms of impact on booster vaccinations and is discussed in more detail in a separate paper.

Causal Forests
To assess treatment effect heterogeneity, we use causal forests (8,9) to predict individual-level treatment effects.The predictors in the model were participants' age, gender, nationality (German vs. Non-German), the size of the address cluster, and the mean distance between the address and the two vaccination sites as predictors.This analysis was conducted using the grf package in R (10) with parameter-tuning.
We use augmented inverse-propensity weighting to estimate a null average treatment effect -0.27 percentage points (SE = 0.17 percentage points).Additionally, we find no evidence of treatment effect heterogeneity; an omnibus significance test fails to reject the null hypothesis of homogeneity at p-value = 0.93.Table S4 depicts the relative importance of the predictors for explaining the minor, non-significant variations in treatment effects across individuals.We present the results of three different treatment effect estimators in Table S5 below.The first row in Column 1 shows the covariate-adjusted difference in vaccination rate between treatment and control for our entire universe (i.e., the average vaccination rate among individuals who received the treatment letter vs. the average vaccination rate among individuals who received the control letter).This estimate makes use of all of the data in our RCT and assumes there are no spillover effects.This is the result we report in the main article.The first row in Column 2 shows the covariate-adjusted difference in vaccination rates for only the randomly selected clusteraddress representatives (i.e., cluster-representatives who received the treatment letter and cluster-representatives who received the control).This estimate isolates the direct effect of the treatment letter and guards against possible spillover effects.The first row in Column 3 shows the covariate-adjusted difference in vaccination rate for households (i.e., the average vaccination rate in address-clusters that received the treatment letter vs. the average vaccination rate in address-clusters that received only control letters).This estimate includes complex spillover effects within households.And while only the estimators in columns 2-3 were pre-registered, the three estimators produce nearly identical treatment effect estimates in all reported analyses.We then use randomization inference to assess how often we would recover the distribution of outcomes we see in our observed data under different conditions.We take the outcome distribution, randomly permute the treatment assignment vector, while keeping the rest of our data frame fixed, for 1,000 iterations.Comparing this synthetic null distribution gives us a standard randomization inference p-value of 0.104 for our overall treatment effect.
Next, we evaluate the impact of iteratively adding a "true" treatment effect in increments of 0.01 percentage points.In other words, all those individuals permuted to a synthetic treatment have the probability of their synthetic outcome variable increased by one percentage point.We use our main estimators to compute the treatment effect coefficient of interest.We repeat for 1,000 iterations to give us a synthetic distribution where we know the true effect.We then assess how likely our observed treatment effect estimates are under this synthetic distribution.We repeat until the probability of observing the treatment effect in our observed data is less than .05.We find that if the true overall effect is at least 0.003 with a constant spillover effect of 0.002, it would appear as observed in our data only 2.0% of the time.
In sum, these analyses provide robust evidence that the treatment effect of the intervention on vaccination is close to zero.

Pre-registered Analysis Plan
We pre-registered the data collection, analysis, and hypotheses at the Open Science Framework (https://osf.io/2trmb)and at ISRCTN (https://www.isrctn.com/ISRCTN59503725).Note that this publication investigates hypothesis H3-2, the remaining analyses are addressed in a forthcoming paper (7).

Data Availability
Since our data contains sensitive health information and the associated socio-demographics could be used to de-anonymize individuals, we cannot provide the full data publicly.Instead, we provide a reduced dataset containing the outcome variable (vaccination uptake), treatment condition (treatment arm and cluster representative status), and cluster ID.To increase anonymity, the data was salted by randomly selecting three vaccinated individuals and inverting their vaccination outcome status, and three unvaccinated individuals and inverting their status.This was done four times: in the treatment arm and in the control arm among address clusters with 25 or more cohabitants, and in the treatment and control arm among clusters with less than 25 cohabitants.Hence, the vaccination outcome for 24 individuals has been changed.This enables interested researchers to reproduce the main results without adjusting for covariates (which do not alter the results) while maintaining the anonymity of all participants.Additionally, we provide all R analysis code and its full output in a separate document.

Statistical Power
The code used for power calculations can be found here: https://doi.org/10.17605/OSF.IO/UH2SD.We created a synthetic dataset using binomial distributions to determine the control and treatment potential outcomes.To take into account the presence of fully vaccinated individuals, we first drew from a binomial distribution with a base rate of 20%.These 20% would always appear as zeros for all potential outcomes.Then we generated different potential outcomes depending on whether the individual received the treatment letter (for direct effect power), are cohabitants with someone who received the treatment letter (for spillover effect power), and an aggregate effect that ignores the possibility of differing direct and spillover effects.We randomly generate treatment conditions and determine if our estimator recovers significant treatment effects over 500 such iterations.
Based on these simulation-based power calculations, we are able to detect a minimal direct effect of 2.6 percentage points, a spillover effect of 1.5 percentage points, and an overall effect of 1.3 percentage points with approximately 80% statistical power (alpha = 0.05).We projected that up to 20% of the control group will get vaccinated at these events.

Limitations
As mentioned in Section 1.1, it is not possible to have complete vaccination information for all Ravensburg residents in our sample.We only tracked vaccinations at the city-organized public events.We investigated the possibility of displacement of vaccinations and effects of wait lines in a separate paper.
The first three events had more demand than the city anticipated, which resulted in long lines and some latecomers being turned away due to a limited availability of doses.For additional information, please see the coverage in the regional German newspaper, Schwaebische Zeitung (11).
To ensure that media reporting did not compromise our experimental design, the Mayor of Ravensburg reached out to local media ahead of time and asked news outlets to delay coverage of the monetary incentives until after the experiment.We monitored local and national media outlets throughout the experiment and found that there was no press coverage of the financial incentives.
Photographs illustrating the vaccination events and the long lines outside can be provided by the authors upon request.

Systematic Literature Review
A systematic review of existing research yielded no studies that discuss policy makers' knowledge about the efficacy of monetary incentives.We searched the database, Web of Science, to identify English-language, peer-reviewed, scientific publications (published before 12/31/2022) based on the following search query: This search yielded 483 publications.We used the AI-based ASReview tool (v.1.1;12, 13) to screen the title and abstract of each publication.We then manually coded 15.5% (i.e., 75) of titleabstract-combinations as to whether they discussed policy makers' knowledge of the efficacy of monetary incentives.On this basis, the trained automation tool ranked the remaining studies by their relevance.As outlined, we could not identify a single study that empirically assessed policy makers' knowledge on the efficacy of monetary incentives.

Methods and Materials
We conducted an online survey to collect policy makers' predictions of the impact of our treatment on vaccination uptake.We emailed 815 mayors of German cities with more than 30,000 inhabitants and heads of German counties.Around 10% of respondents reported that their locality had less than 30,000 residents.This could be that some of them were asked to fulfill the tasks of a district or they were encouraged to participate by the mayor of Ravensburg.81 emails bounced back because they were no longer valid.88 policy makers participated in the survey (44% of them were experts within the administration who were forwarded the survey email by their mayor/county head).
In the survey, the policy makers were presented with a description of our experiment and a copy of the treatment letter.Subsequently, they were asked to forecast what proportion of those who received a letter with and without a monetary incentive would get vaccinated.Additionally, we asked them to predict the vaccination rate for those who did not get a monetary incentive but lived in a household with somebody who did get a monetary incentive, as well as the vaccination uptake of households where nobody received a monetary incentive.
This within-person design necessitated exposing our respondents to both the letter with a monetary incentive and the letter without.This design may be vulnerable to demand effect bias and other related cognitive biases, which may induce a participant to predict larger differences between two choices (over no differences).An alternative option to presenting policy makers would be a between-subjects design.However, we felt a between-subjects survey was intractable due to power considerations.For instance, our within-subjects design is well-powered, even without control covariates, to detect effects as small as 4.6 percentage points.Assuming similar variances in a between-subjects design, we would need 670 respondents (or a, likely unattainable, 82% survey completion rate).
A within-subjects design is also more aligned with the decision-making environment a policy maker is likely to find themselves in (choosing between two options, a letter with incentives, or one without them), rather than two policy makers in different municipalities choosing whether to adopt a letter with no knowledge of the alternatives.Research design handbooks predominantly caution about three key mechanisms that could distort treatment effect estimates in withinsubject designs: practice/learning, sensitization, and carryover (e.g., 14, 15) -all of which are not applicable in our policy choice context.Indeed, within-subject designs appear to be standard practice among other expert surveys (16,17,18).

Fig. S1. Policy makers' predictions as percentage differences between treatment and control group.
Overall, policy makers predicted a high proportion of vaccinations.They predicted, on average, that 27.5% (SD = 20.1) of people in the no-incentive condition would get vaccinated and 42.8% (SD = 24.0) in the monetary incentive condition would get vaccinated (see Fig. S2).In other words, the participating policy makers estimated a positive treatment effect of 15.3 percentage points (SD = 15.7,t(86)=-9.06,p<.001).

Fig. S2. Policy makers' predictions of vaccine uptake by individuals who did or did not receive a monetary incentive
It should be noted, for the sake of transparency, that the policy maker survey was conducted in March 2022, after the field experiment was completed in December 2021.However, the results of the field experiment were not shared with anyone outside of the co-authors of this manuscript.
In other words, even collaborators in the Ravensburg city administration were not informed of the results.

Prediction Heterogeneity
Below, we present a series of subgroup analyses across participant characteristics.We find that the policy makers' predictions were fairly homogenous.In all, no subgroup was statistically significantly different from any other subgroup.

Investigating Policy Maker Non-Response
It is possible that our respondents are systematically different from non-respondents.To assess how nonresponse or selection bias may impact our results, we pursue a bounding exercise.We take all non-respondents and simulate different levels of non-response bias.Specifically, for all 727 policy makers who did not respond to our survey, we generate possible predictions.The first row in Table S9 assumes maximal nonresponse bias; in other words, we assume that the average non-respondents' prediction was equivalent to the actual results of the financial incentive RCT in Ravensburg.This is operationalized as treatment effects drawn from a normal distribution with mean equivalent to the mean treatment effect we observed in the RCT and the variance set to the Neymann variance measured in the RCT.
The first column of Table S9 depicts the simulated mean prediction among non-respondents.Since the first row assumes maximal non-response bias, it is equivalent to the null result in our RCT.We then combine the real response data from respondents with the simulated data from non-respondents and calculate the prediction we would have observed under maximal nonresponse bias.The second column depicts this result.Specifically, we see that even with maximal nonresponse bias, policy makers would be significantly (as seen from the confidence interval in column 3) misaligned on the null effect of financial incentives.
With each row, we relax the magnitude of nonresponse bias by 2 percentage points.So for row 2, policy makers who did not respond are still much closer to the null result than our respondents (only 2 percentage points off) but not as preternaturally prescient of the true impact of financial incentives as in the first row.We continue to decrease accuracy (by an iteration of 2 percentage points) until the non-respondent prediction is equal to the mean predicted impact we report in our main text.In all, we see that our results are robust to even moderate to large levels of nonresponse bias.In the wake of the ongoing Corona pandemic, we are exploring local decision-makers' attitudes toward interventions to increase vaccination preparedness.This survey aims to determine how youat a local levelrate the effectiveness of monetary incentives as part of vaccination campaigns.
In the following, we would like to ask you some short questions about a vaccination campaign that the city of Ravensburg carried out with our scientific support.We would like to ask you to share your professional experience here.There are no right or wrong answers.The survey is completely anonymous and takes about 10 minutes.

Thank you for your support! [NEXT PAGE]
Background: The city of Ravensburg has approx.50,000 inhabitants (of which approx.41,000 are adults).The rate of persons with first and second shots in the district of Ravensburg before the start of the vaccination campaign at the end of October 2021 was around 60%. Almost no one had already had a booster vaccination at that time.
The campaign: The vaccination campaign included seven public vaccination appointments (between 11/13/2021 and 12/11/2021), to which all adult residents were invited via mail.One could receive a first, second or booster vaccination on the vaccination dates.All adult residents received one of two different versions of a letter.The two versions of the same letter were sent out on 11/05/2021.Who received which letter was determined at random.
• Version 1 -With voucher: A number of randomly selected adult residents (approx.5,000) were sent the following letter from the city of Ravensburg.These residents were given the opportunity to receive a 20 Euro voucher if they were vaccinated on one of the vaccination dates.In addition, all recipients had the chance to receive a second 20 Euro voucher if more than 900 adults from Ravensburg were vaccinated on the dates.Those who had already been fully vaccinated could pass the letter on to an unvaccinated person and thereby also receive the chance to win the second 20-euro voucher.
• Version 2 -Without voucher: All other residents received a regular letter without financial incentive.
Version 1 (with voucher) of the cover letter Version 2 (without voucher) of the cover letter

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As we move forward, we would like to ask you some questions about this vaccination campaign.We would first like to ensure that we have made the details of the campaign sufficiently clear.To this end, we ask you to select below the one of the four statements that describes the said vaccination campaign as precisely as possible: 1.All Ravensburg residents were promised a 20-Euro vaccination voucher if they got vaccinated.
2. Only a few randomly selected residents were promised a 20-Euro vaccination voucher if they got vaccinated.The voucher was not transferable.
3. Only a few randomly selected residents were promised a 20-Euro vaccination voucher if they got vaccinated.In the case that more than 900 residents were vaccinated, a second voucher was promised.Those randomly selected people who were already fully vaccinated could still receive the second voucher if they had transferred the letter to another person.
4. No vouchers were offered to the inhabitants of Ravensburg.
[Explain the design again if participants selected the wrong answer] [NEXT PAGE] First, we would like you to give us your assessment regarding all types of vaccinations (first, second, and booster).
Q1.1: What percentage of adult residents who received a letter with a voucher were vaccinated on one of the seven events (first, second, and booster vaccination)?
Your estimate: 0-100% Q1.2:What percentage of adult residents who received a letter without a voucher were vaccinated on one of the seven events (first, second, and booster vaccination)?

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In conclusion to this survey, we would like to collect some information about you briefly:

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How many inhabitants does the local authority for which you work have?like to share any additional information about the vaccination campaign with us, please use the following free text field: Section 3: References 1. Ministry of Social Affairs, Health and Integration Baden-Wuerttemberg, Vaccination data 12/27/2020 -11/07/2021 by district in Baden-Wuerttemberg (Impfdaten 27.12.2020- 11/22/2021 The federal health ministry restricts the delivery of the BioNtech/Pfizer vaccine to reserve it for minors under the age of 18 and offers Moderna vaccines as an alternative.11/26/2021 The German air force starts to transport patients to other parts of the country and neighboring countries due to overcrowded hospitals.11/27/2021 The federal health ministry officially confirms the first cases of Omicron in Germany and further restricts travel from other countries.11/30/2021 Chancellor Scholz and the heads of German state governments announce that the 2G rule has become the nationwide standard and confirm plans for a vaccination mandate.12/04/2021 The regional health ministry of Baden-Wuerttemberg introduces the "2G+ rule", restricting access to most shops and restaurants to people who either 1) received the first and second dose or 2) recovered from COVID-19 and can provide a recent negative test.12/08/2021 The state of Baden-Wuerttemberg opens further vaccination centers exclusively for booster vaccinations.12/21/2021 The national government and the heads of German state governments announce stricter social restrictions.Starting on 12/28/2021, private gatherings of vaccinated and recovered persons are restricted to a maximum of ten persons; private gatherings of unvaccinated persons are limited to the person's own household and a maximum of two persons from another household.The Standing Committee on Vaccination recommends booster vaccinations for all persons older than 18 years of age, three months after the second dose.

without appointment) Are you already fully vaccinated? Then
motivate your environment from Ravensburg now to a vaccination on one of the mentioned dates.

Get your COVID-19 vaccination now, receive a 20 Euro voucher and secure the chance to win another 20 Euro!
11e city of Ravensburg is giving away shopping vouchers.You are one of 5,000 randomly selected people from Ravensburg who can receive these limited vouchers.Then please go for vaccination on one of the following dates.In this section, we describe the data generative process underpinning our main outcome variable of interest, vaccination uptake.All outcomes were collected during the following vaccination events:11/13/2021, 11/19/2021, 11/20/2021, 11/26/2021, 12/10/2021 and 12/11/2021.
Hello [ACADEMIC TITLE] [NAME],Congratulations! Are you not yet fully vaccinated?Then please go for vaccination on one of the following dates.You will receive your 20 Euros voucher for this by mail after the vaccination.-13.11.: Saturday, 9 a.m.-2 p.m. -19-20.11.: Friday, 3-9 p.m. or Saturday, 9 a.m.-2 p.m. -26-27.11.: Friday, 3-9 p.m. or Saturday, 9 a.m.-2 p.m. -10-11.12.: Friday, 3-9 p.m. or Saturday, 9 a.m.-2 p.m. Place: City Hall, Marienplatz 26, 88212 Ravensburg (without appointment) After your vaccination on one of these dates, you also have a chance to win: if more than 900 people from Ravensburg get vaccinated on these dates, you will receive your 2nd voucher worth 20 Euros by mail.Are you already fully vaccinated?Then motivate your environment from Ravensburg now to a vaccination on one of the mentioned dates.If this person shows your letter at one of the mentioned dates, you secure your chance to win: If more than 900 people from Ravensburg get vaccinated, you will receive your 20 Euros voucher by mail.Available are vaccines from BioNTech/Pfizer, Moderna and Johnson & Johnson.Please bring, if available, vaccination certificate, identity card and insurance card of the health insurance.For more information, please visit the following website: [INDIVIDUAL LINK].

Table S1 .
Descriptives.Counts, percentages, and means for the study's treatment arms and the total population.

Table S2 .
Treatment Effect by Vaccination Type.Covariate-adjusted OLS regression estimates of the effects of the treatment on all vaccinations, first dose vaccinations, primary vaccinations (first and second dose), and booster vaccinations.

Table S4 . Relative importance of predictors for treatment effect heterogeneity.
Causal forest estimation.